1. Field of the Invention
The present invention relates to a fuzzy neuron device which applies the fuzzy theory and the neuron theory.
2. Description of the Related Art
A neuron computer is known as one of the computer systems. The neuron computer has a structure simulating the human brain. In the neuron computer, a neuron unit (neuron element) corresponds to a neuron of the human brain. Neuron units are combined with each other by a communication line which is called a synapsis, thereby constituting a neural network.
A plurality of weighted signals are input to each neuron unit, and when the sum of the input signals exceeds a predetermined threshold value, the neuron unit outputs a signal. In a neuron unit represented by such a mathematical model, appropriate values obtained from learning are allotted to the weighting coefficient and the threshold value, respectively.
The neuron computer is used for the purpose of pattern recognition. For example, it is used for the purpose of recognition of a hand-written letter. In this case, the actual neuron computer needs to have a neuron network composed of a plurality of layers. A multiplicity of neuron units are therefore required, so that it takes much labor and time to set the weighting coefficient and the threshold value to each neuron unit from learning.
To solve this problem, a new neuron device in which the fuzzy theory is applied to the neuron theory has been proposed (see, for example, A fuzzy Neuron Chip and Its Application to a Pattern Recognition System, IFSA '91 (1991)).
When the new fuzzy neuron device is used so as to recognize a hand-written letter, a characteristic extracting line (hereinunder referred to simply as "line") is used in order to extract the characters of the letter as the object of recognition. A plurality of synapses are connected to the line, and of the signals output from these synapses, the signal having the minimum value is output as the output signal of the fuzzy neuron unit. This output signal represents the recognized letter.
The system for incorporating the fuzzy neuron device into hardware is largely classified into two systems. One is a serial system and the other is a parallel system.
In the serial system, the signals necessary for computation are converted into digital signals, and the digital signals are processed by a digital circuit in time series.
In the parallel system, the signals necessary for computation are processed as they are in parallel.
The serial system is advantageous in that the number of lines and the number of synapses are freely extensible (can be increased) but disadvantageous in that the processing speed is slow. On the other hand, in the parallel system, although the processing speed is high because the signals are processed in parallel, the extension of the number of lines and the number of synapses is difficult, so that the number of lines and the number of synapses must be determined at the stage of designing the circuit.
FIG. 1 shows the circuit structure of a conventional fuzzy neuron device of a parallel system.
A plurality of synapses S are connected to a minimum value computing circuit 10. In each synapsis S, a membership function based on the fuzzy theory is stored, and each synapsis outputs a signal which corresponds to an input signal on the basis of the membership function. The minimum value computing circuit 10 extracts the signal taking the minimum value from among the signals output from the synapses S and outputs the extracted signal to an output terminal 12. In this device, three synapses correspond to one line. Of the three synapses, one is an excitatory synapsis, another is an inhibitory synapsis and the other is an exitatory or inhibitory synapsis.
Eight lines, for example, are input to the fuzzy neuron device (chip). That is, the fuzzy neuron device has twenty-four synapses.
In the case of using the fuzzy neuron device for the recognition of a letter, such a reading device as shown in FIG. 2 is used in order to read the pattern of the letter. In FIG. 2, a plurality of optical sensors P are arranged in a matrix. One line corresponds to a vertical or horizontal group of optical sensors P. The signal of each line is input to a predetermined number of (three) synapses shown in FIG. 1.
The parallel system shown in FIG. 1 is advantageous in that the processing speed is high, as described above. However, the number of lines and the number of synapses must be determined at the stage of designing the circuit, and it is very difficult to increase or reduce the number thereafter.
In a conventional fuzzy neuron device, it is therefore not easy to increase the number of synapses or the like by connecting a plurality of fuzzy neuron devices to each other. Although the extension is not impossible if another device (circuit) is inserted between the fuzzy neuron devices, it leads to complication of the system and a rise in cost.